Dicks, Matthew and Chavula, Josiah (2021) Deep Learning Traffic Classification in Resource-Constrained Community Networks, Proceedings of IEEE AFRICON, 13-15 September 2021, Arusha, Tanzania, IEEE.
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Abstract
Community networks are infrastructures that are run by the citizens for the citizens. These networks are often run with limited resources compared to traditional Internet Service Providers. For such networks, careful traffic classification can play an important role in improving quality of service. Deep learning techniques have been shown to be effective for this classification task, especially since classical approaches struggle to deal with encrypted traffic. However, deep learning models often tend to be computationally expensive, which limits their suitability for low-resource community networks. This paper explores the computational efficiency and accuracy of Long Short-Term Memory (LSTM) and Multi-Layer Perceptron (MLP) deep learning models for packet-based classification of traffic in a community network. We find that LSTM models attain higher out-of-sample accuracy than traditional support vector machines classifiers and the simpler multi-layer perceptron neural networks, given the same computational resource constraints. The improvement in accuracy offered by the LSTM has a tradeoff of slower prediction speed, which weakens their relative suitability for use in real-time applications. However, we observe that by reducing the size of the input supplied to the LSTMs, we can improve their prediction speed whilst maintaining higher accuracy than other simpler models.
| Item Type: | Conference paper |
|---|---|
| Uncontrolled Keywords: | Network traffic classification, deep learning, community networks |
| Subjects: | Networks Networks > Network performance evaluation Networks > Network performance evaluation > Network performance modeling Networks > Network services > Network management Networks > Network services > Network monitoring Networks > Network performance evaluation > Network performance analysis Networks > Network performance evaluation > Network measurement |
| Date Deposited: | 13 Dec 2021 07:46 |
| Last Modified: | 13 Dec 2021 07:46 |
| URI: | https://pubs.cs.uct.ac.za/id/eprint/1519 |
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